Method and apparatus for measuring exertion endurance

ABSTRACT

The present invention relates to a method and an apparatus for measuring exercise condition, especially a method for measuring an exertion endurance indicator representing exercise condition of a subject to be measured, such as maximal oxygen uptake or any such exertion endurance indicator representing exercise condition. The method is characterized in that, in the method a predetermined calculation formula is used preferably by means of a neural network, to which formula physiological parameters representing the subject to be measured are supplied. The input parameters comprise at least one or more of the following physiological parameters, such as sex, age, height, weight. One or more output parameters representing the exertion endurance indicator representing the exercise condition of the subject to be measured are obtained as a result from the calculation formula. In addition to the physiological parameters, one or more resting heartbeat parameters measured specifically from resting heartbeat are used as input parameters of the calculation formula. In the preferred embodiment of the invention, the calculation formula is formed by means of a neural network construction.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The invention relates to a method for measuring exercise condition,especially to a method for providing an exertion endurance indicatorrepresenting an exercise condition of a subject to be measured, such asmaximal oxygen uptake or any such exertion endurance indicatorrepresenting an exercise condition.

The invention further relates to an apparatus for measuring exercisecondition, especially to an apparatus for measuring an exertionendurance indicator representing an exercise condition of a subject tobe measured, such as maximal oxygen uptake or any such exertionendurance indicator representing physical fitness.

2. Description of the Prior Art

Condition classification representing exercise exertion based onmeasuring maximal oxygen uptake is used as an indicator of physicalexercise condition that is, exertion endurance, for example to measurehuman physical performance, such as exertion endurance.

Prior art solutions for determining and measuring an exercise conditionare either direct exertion measurement or indirect measurement. Indirect exertion measurement, the maximal oxygen uptake ability ismeasured directly from respiratory gases under maximum exertion by meansof a running mat or a bicycle ergometer, for example. In indirectmeasurement, the work performed is measured within a specific period oftime, such as in so-called Cooper test where a distance run during 12minutes is measured. In both known methods the measurement of exercisecondition takes place by measuring an active performance, whereforethese methods are laborious, difficult and expensive to arrange in orderto determine condition. Average resting heartbeat is considered to beone indicator of condition, but it does not give reliable results as thecorrelation of resting heartbeat to maximal oxygen uptake ability isonly at the rate of 0.4 to 0.45. Other heartbeat parameters also do notattain better correlations to maximal oxygen uptake ability.

OBJECTS AND SUMMARY OF THE INVENTION

An object of the present invention is to provide a new method that willavoid the problems associated with prior art methods.

This object is attained in accordance with a method of the invention inwhich a calculation formula obtained by means of a neural network isused for measuring an exertion endurance indicator representing anexercise condition, to which formula input parameters representing thesubject to be measured are supplied. Such input parameters comprise atleast one or more of the following physiological parameters: sex, age,height, weight. One or more output parameters representing the exertionendurance indicator representing the fitness of the subject to bemeasured are obtained from the calculation formula. The neural networkconstruction used for formulating the calculation formula is trainedwith a sufficiently large number of real measuring results comprisingsimilar input parameters and one or more similar output parameters. Inaddition to the physiological parameters, one or more resting heartbeatparameters measured specifically from resting heartbeat are used as theinput parameters of the calculation formula. Similar resting heartbeatparameters are used in the training of the neural network used informulating the calculation formula of the exertion endurance indicatorrepresenting an exercise condition.

This object is attained in a second embodiment of the present inventionusing the method of the invention. In accordance with the method,physiological input parameters representing the subject to be measuredare supplied to a predetermined calculation formula, the inputparameters comprising at least one or more of the followingphysiological parameters, such as sex, age, height, weight. From thecalculation formula, one or more output parameters representing theexertion endurance indicator, such as maximal oxygen uptake ability orany other such exertion endurance indicator representing the exercisecondition of the subject to be measured, are obtained. In addition tothe physiological parameters, one or more resting heartbeat parametersmeasured from resting heartbeat are used as input parameters of thecalculation formula

An apparatus according to the invention comprises means for detectingresting heartbeat and sending resting heartbeat data to a calculationunit. Means are provided for supplying physiological parametersrepresenting the subject to the calculation unit. The calculation unitgenerates an exertion endurance signal representing an exercisecondition, such as maximal oxygen uptake ability, conditionclassification or any such physical condition indicator based on thephysiological parameters and resting heartbeat data supplied to it. Adisplay and/or a memory are provided for indicating and/or storing thephysical condition data.

The method and apparatus of the invention are based on the idea thatresting heartbeat parameters are used as input data of exercise exertionendurance, and a calculation formula predetermined preferably by meansof a neural network is used. Resting heartbeat parameters and humanphysical parameters are supplied to the formula as input data andmaximal oxygen uptake (for example) is calculated as output datarepresenting human physical condition, that is, exertion endurance. Informulating the calculation formula, the neural network, if used, wouldbe trained by corresponding data by using an extensive real measurementmaterial. Different parameters of a person's heartbeat and heartbeatvariation measured during a few minutes are needed as measurement dataand, in addition to the parameters obtained from their heartbeat, humanphysical measurement parameters, such as weight, height, age and sex,are utilized. The data measured from resting heartbeat and personalpre-data are provided to the calculation formula as supply data. Whendetermining the calculation formula by a neural network, different ruleshave been made by means of fuzzy logic, that is, the effect of differentvariables or variable combinations on the end result is made fuzzy. Thecalculation formula determined by means of the neural network calculatesby weightings obtained on the basis of training material the maximaluptake ability of a person from the new supply data and determines acorresponding condition class.

Neural networks are known per se, and they have been used previously formeasuring a patient's condition of health, the seriousness of a person'sinfarct, the risk of death for elderly persons or a person's bloodpressure. These solutions have been disclosed for example in EP-555591.

DE-4307545 further discloses an apparatus that determines the locationand the extent of a person's infarct. This apparatus employsmulti-channel EKG measurement, and infarct determination is based on thetrained use and classification of neural network construction in theapparatus.

EP-650742 discloses an apparatus which controls a pacemaker, i.e. adefibrillator by means of a neural network. This apparatus measures theEKG curve, compares it to the data bank and decides if a pacemaker pulseis needed.

WO-92/03094 discloses an apparatus in which a patient's heart isdiagnosed by means of heart sounds by using a neural networkconstruction.

U.S. Pat. No. 5,251,626 discloses an apparatus for detecting andclassifying arrhythmias which is similar to that in EP 650742 citedabove.

U.S. Pat. No. 5,280,792 discloses an apparatus for detecting andclassifying arrhythmias that is similar to what is disclosed in EP650742 and U.S. Pat. No. 5,251,626 cited above.

DE-4338958 discloses an apparatus and a method for determining aperson's optimal exercise heartbeat. In this solution an optimalexercise heartbeat is determined by using an iterative method wherefirst an initial heartbeat level/load is determined by using knownformulae and then heartbeat level under load is measured. The differencebetween assumed and measured heartbeat level is used to optimize thecorrect heartbeat level/load level. The result can be further specifiedby taking other variables and factors into account by using a neuralnetwork and/or a multi-variable analysis. The disadvantage of thesolution is that heartbeat has to be measured during loading. Thissolution refers to a maximal oxygen uptake ability, but in this solutionmaximal oxygen uptake is used as input data and not as calculationoutput data as in the solution of the invention.

The references cited above are in no way related to measurement ofexertion endurance.

The above-mentioned prior art solutions all use heartbeat measurementvalues measured under exertion without any more specific heartbeat dataanalyses, whereas according to the preferred embodiment of the solutionof the invention, one or more RR interval parameters, such as meanheartbeat interval, standard deviation of heartbeat intervals or maximumheartbeat interval, are calculated from resting heartbeat.

Several advantages are attained with the method of the invention. Themethod of the invention is very accurate, simple, advantageous in itscosts and easy to implement as a method for measuring exercise conditionor exertion endurance, such as maximal oxygen uptake ability. The methodof the invention is very useful for testing and determining the anexercise condition of ordinary persons who exercise because it is easyto record resting heartbeat during a few minutes, determine the physicalparameters and supply them to the necessary measurement apparatus as noexertion test need be done. The new, accurate and easy method of theinvention can also be used by sportsmen/sportswomen for monitoringchanges in exercise condition. More accurate direct tests can be madeless often as reference. The method of the invention will also saveexpenses, which are considerable in a direct test. By the method of theinvention, a correlation at the rate of 0.97 has been obtained as aresult between the maximal oxygen uptake ability calculated by theneural network based calculation formula and on the other hand, oxygenuptake ability measured with the direct method. By means of theinvention, it is possible to determine a person's physical exercisecondition and performance reliably and easily without maximal exertion.The method of the invention can be implemented, for example, by means ofa heartrate monitor worn on a wrist, a health watch or in connectionwith some other such apparatus.

BRIEF DESCRIPTION OF THE DRAWINGS

In the following, the invention will be explained in more detail withreference to the accompanying drawings, wherein

FIG. 1 shows a graphical representation of the neural networkconstruction,

FIG. 2 shows a neural network construction in a matrix form,

FIG. 3 shows one neural network construction used for determining acalculation formula of physical condition,

FIG. 4 shows a membership function in a fuzzy group,

FIG. 5 shows coefficient and bias matrices determined on the basis ofthe neural network construction and the extensive test material suppliedthereto,

FIG. 6 shows an apparatus for applying the method according to theinvention,

FIG. 7 shows resting heartbeat, and

FIG. 8 shows the apparatus used by a human being.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

FIG. 7 shows a typical EKG signal caused by heartbeat. P, Q, R, S, T andU waves can be identified in each signal by accurate measuring. The Rwave is formed by polarization of the ventricles of the heart andgenerally represents a peak value. Peak value R represents the maximumpoint of the EKG signal and the interval R-R represents a beat interval.The beat can be measured from a pressure pulse or optically.

FIG. 6 shows an example of a heartrate monitor applied in the invention.Preferably the heartrate monitor comprises a heartbeat transmitter unitA attached to the chest of a person to be examined and a receiver unit Bfor receiving heartbeat signals wirelessly from the transmitter unit.Heartbeat measurement and analysis functions are included in thereceiver unit. To be more specific, the exemplary equipment shown inFIG. 6 includes a transmitter unit A comprising an EKG pre-amplifier 1to which heartbeat identifying electrodes 1 a, 1 b are connected. Thesignal of the pre-amplifier 1 is amplified in an AGC amplifier 2 andfurther in a power amplifier 3 where a heartbeat signal controllingwindings 4 is produced, in which signal the interval between the pulsesin the signal is the same as the interval of heartbeats. A magneticfield varying at the rate of the heartbeat is thus generated to thewindings 4. The magnetic field provided by a transmitting coil orwindings 4 is detected by a coil 5 of the receiver unit B forming theother part of the equipment. The coils 4 and 5 form an inductivecoupling, which operates by means of the magnetic field, between thetransmitter unit A and the receiver unit B. The received signal isamplified by means of amplifier circuits 6 and 7 in the similar way asin the transmitter. The amplified signal is conveyed to a microprocessor8 that calculates from the heartbeat signal desired factors. A memory 9and a display device 10 are attached to the microprocessor. In theserespects the apparatus is similar to known heartrate monitors.

New features in the equipment shown in FIG. 6 include a calculation unit200 and supply means 201. In a practical implementation the calculationunit 200 can be implemented as a program of the microprocessor. Thecalculation unit 200 is supplied with resting heartbeat parameterscalculated on the one hand by the microprocessor 8 and on the otherhand, by supply means 201. A person supplies physiological inputparameters describing the person, such as sex, age, height, weight.

The invention thus relates to a method for measuring physical exercisecondition, preferably human physical exercise condition, or exertionendurance of a subject to be measured. In the method a specificpredetermined calculation formula is used in the calculation unit 200 towhich physiological input parameters representing the subject to bemeasured are supplied, said input parameters comprising at least one ormore of the following physiological parameters: sex, age, height,weight. From the calculation formula one or more output parameters, suchas maximal oxygen uptake ability representing the condition of thesubject to be measured are obtained as a result. In accordance with theinvention, the method is such that in addition to physiologicalparameters one or more resting heartbeat parameters measured from theresting heartbeat are used as an input parameter of the calculationformula. The calculation formula may be implemented in the equipmentshown in FIG. 6 by the calculation unit 200 which can be integrated as aprogram of the microprocessor 8 of the heartrate monitor receiver unitB.

In a preferred embodiment of the invention, the method is such that theresting heartbeat is measured during a period of a few minutes, mostpreferably during a period of 2 to 5 minutes. On the one hand,measurement is easy to perform, but on the other hand, it is long enoughfor the measurement to be reliable when information about heartbeatvariation is obtained.

In a preferred embodiment, the method is such that one or more of thefollowing resting heartbeat parameters such as mean heartbeat interval,standard deviation of heartbeat intervals, maximum mean heartbeatinterval, are determined as input parameters from resting heartbeat.These parameters an be calculated in the microprocessor 8 of thereceiver unit B of the heartrate monitor, for example.

In a preferred embodiment of the invention, the method is such that bycombining input parameters, one or more input parameter combinations areformed. Some examined input parameters are shown in Table 1.

TABLE 1 Features, i.e. input parameters used in the method FeatureExplanation Age The age of a test person with an accuracy of one yearSex The sex of a test person Weight The weight of a test person with anaccuracy of 0.2 kg Height The height of a test person with an accuracyof 1 cm Fuzzy_1 The value of membership function in a fuzzy set ‘olderpersons’ Fuzzy_2 The value of membership function in a set of‘non-medium weight persons’ Fuzzy_3 The value of the membership functionin a set ‘great heartbeat intervals on the average’ Low_ave_3 Averagedmaximal respiration modulation to heartbeat frequency PRC99 Theaccumulation value (percentage) of heartbeat histogram at 99% MaxMaximum heartbeat interval Mean Mean heartbeat interval Sdev Standarddeviation of heartbeat intervals

With reference to Table 1, one or more different rules are formed bymeans of fuzzy logic with which rules the effect of one or more inputparameters and/or one or more input parameter combinations on the outputparameter, that is, on maximal oxygen uptake ability representingcondition is made fuzzy. The unit liter per minute (1/min) and/ormilliliter per kilogram per minute (ml/kg/min) can be used as a unit ofmaximal oxygen uptake ability.

In a preferred embodiment of the invention with reference to FIG. 3, themethod comprises preclassification and an actual calculation after it.In said preferred embodiment, the method is such that preclassificationis carried out by means of one or more physiological input parameters,in which the possible solution area is searched on which the value ofthe output parameter to be calculated is estimated to lie, and that atthe actual calculation stage, the input parameters of resting heartbeatare also used, whereby at the calculation stage, the value of the outputparameter representing the physical condition of the subject to bemeasured is moved towards the correct value on the basis of the inputparameters of resting heartbeat.

In the preferred embodiment of the invention, one or more inputparameters which are made fuzzy are also used in addition to thephysiological input parameters. This is illustrated in FIG. 4 whichshows membership function in a fuzzy set “old”. FIG. 4 is a graphicalrepresentation of the concept membership function of fuzzy logic. Themembership function shows at how great proportion in this example aperson of a certain age belongs to the set old. Fuzzy logic represents away of thinking where membership to a certain set is a continuousconcept. A middle-aged person belongs partly to the set young and partlyto the set old. By using fuzzy logic, new parameters, that is, featuresof heartbeat parameters and a person's weight, height, age and sex areformed to the input vector VR which can be seen in FIG. 2. The inputvector VR comprises the input parameters present in Table 1.

As it concerns measurement of human physical exercise condition, thatis, of exertion endurance, the method in the preferred embodiment issuch that the value of maximal oxygen uptake ability corresponding toinput parameters and/or condition classification representing oxygenuptake ability or any other such value representing physical exerciseexertion endurance is obtained as an output parameter as a result ofcalculation.

In a preferred embodiment of the invention, known empirical data is usedin the calculation formula, and empirical data is provided to thecalculation formula according to fuzzy rules.

In a preferred embodiment of the invention, one or more of the followingpieces of information are used as empirical data; “an older person isprobably in a poorer condition”, “the weight of a person correlates withthe condition of the person best in the person set non-medium weight”,“the person with a great mean heartbeat interval is probably in a goodcondition”.

In a preferred embodiment of the invention, the method is implemented bythe calculation unit 200, and the calculation unit 200 implementing thismethod is integrated into a heartrate monitor receiver unit mounted B toa wrist strap.

In the preferred embodiment of the invention the resting heartbeat ismeasured by means of measuring means 1 to 4 in connection with thereceiver unit or other such apparatus. A wireless or contact couplingmay be used to couple the transmitter and receiver units. Thephysiological input parameters, such as sex, age, height, weight, aresupplied to the calculation unit 200 included in this heartrate monitorreceiving unit B or any other such apparatus by means of supply means201 in connection with the apparatus or connected to it by a wireless orcontact coupling. The calculation result is shown on the display 10and/or is stored in the memory 9 as shown in FIG. 6. In the preferredembodiment of the invention, the calculation result is shown on thedisplay 10 included in the heartrate monitor receiver unit B or anyother such apparatus. In the preferred embodiment of the invention, theheartrate monitor receiving unit or any other such apparatus ispositioned on a person's wrist.

The heartrate monitor transmitter unit A and associated components 1 to4 can be, as in FIG. 6, inductively coupled to the heartrate monitorreceiver unit B, but they could also be in the case of the heartratemonitor receiver unit B, or otherwise in a wireless coupling, or in someother contact coupling to the heartrate monitor receiver unit B.

The supply means 201 of physiological input parameters, such as sex,age, height, weight, can also be in a wireless or contact coupling tothe heartrate monitor receiving unit B, but they could be coupled insome other way, too. The simplest and most reliable method known to theapplicant is to integrate the supply means 201 in connection with thereceiver unit B of the heartrate monitor.

The neural network construction NN shown in FIG. 3 is utilized in apreferred embodiment of the invention. In that case the method accordingto the preferred embodiment of the invention uses a calculation formulawhich is obtained by means of the neural network construction NN towhich input parameters representing the subject to be measured aresupplied. The input parameters comprise at least one or morephysiological parameters, such as sex, age, height, weight. Thecalculation formula provides one or more output parameters representingthe exercise condition, that is, exertion endurance of a subject to bemeasured. The neural network construction NN used for forming thecalculation formula has been trained with a sufficiently large number ofreal measurement results, such as clinical measurement results of 200test subjects comprising corresponding input parameters and one or moresimilar output parameters as those that the calculation unit 200 uses incalculation. According to the invention, one or more resting heartbeatparameters measured from resting heartbeat are used as input parametersof the calculation formula in addition to the physiological parameters.Corresponding resting heartbeat parameters are used in training theneural network NN used for formulating the calculation formula.

The calculation matrices obtained as a result of training the neuralnetwork construction are realized as a calculation formula by usingknown activation functions, and multiplication and addition.

With reference to FIGS. 1 to 3 in particular, the neural network NNcomprises an input layer, an output layer and a hidden layer. There maybe several neurons in each layer. Each cell parameter forms one inputneuron in the input layer. There are as many neurons in the output layeras output variables. The number of neurons in the intermediate layerdepends on the structure of the network. The signals of the neurons inthe network are calculated by combining the variables and/or neurons ofthe previous layer using linear or non-linear activation functions.

A simple neural network construction NN is disclosed in FIG. 1. Thestructure includes an input layer, one hidden layer and an output layer.There are three cells in the input layer that are not neurons butillustrate values of the input vector. In the hidden layer there are twoneurons to which the cells in the input layer are completely connected.The connections comprise weighting coefficients with which the strengthof the signal is weighted in summing in the following layer. Each hiddenlayer and input layer can be associated with a bias vector which hasbeen omitted in this presentation for the sake of simplicity. FIG. 1also shows the algebraic formulae of the neural network example.

In FIG. 2 the neural network construction NN is shown by using matrixand vector formulae. Bias vectors b which improve the operation of theneural network construction NN are also added to the figure. Thedetermination of the weighting coefficients of the neural network takesplace by using general training algorithms of neural calculation. Acoefficient matrix and a bias vector b are obtained for each neuronlayer as a result of the training of the network. The neural network canafter this be realized by using mathematical functions, multiplicationand summing in a simple programmable form as a computer program.

It was stated earlier that preclassification and actual calculationafter it are used in the method. This division is a result of the neuralnetwork construction NN of FIG. 3 used in the preferred embodiment. Theneural network construction NN comprises two sections: a preclassifierand an actual calculation structure. Physiological and fuzzy featuresaccording to Table 1 are used in the preclassification. An advantage ofpreclassification is that the physiological features define the possiblesolution area, a small woman, for example, cannot have the lung capacityof a big man.

The model of the neural network NN is shown in FIG. 3 where the featuresshown in Table 1 are input quantities. The size of the correlation tothe quantity to be measured, that is, to maximal oxygen uptake abilityis examined in selecting the features.

A backpropagation method, for example, or any such suitable method hasbeen used in the training of the neural network NN. Coefficient and biastables in the matrix form according to FIG. 5 are obtained as a result.The matrices of the preclassifier have an identifier F and the matricesof the basic structure B. Weighting coefficient matrices are identifiedby w index and bias vectors by b index. The numerical index notifies thenumber of the layer. The indication p represents features, that is,input parameters.

The invention can be disclosed briefly in such a manner that at first aperson's heartbeat at rest is measured for example in a sitting positionduring a few minutes. Various heartbeat and heartbeat intervalparameters, such as mean heartbeat, standard deviation, maximum ofsuccessive beats and minimum intervals and/or other parameters, arecalculated from the heartbeat data by means of software in themicroprocessor 8, for example. These parameters are used as supply datain the calculation unit 200. Other data, such as age, sex, weight andheight, obtained from the supply means 201 is also used a supply datafor the calculation unit 200. By combining this data by using differentrules, new parameters are derived which can further be made fuzzy bymeans of fuzzy logic. As an exemplary rule it could be mentioned thatthe condition or maximal oxygen uptake ability of a short and heavyperson is probably not good.

With reference to FIGS. 6 and 8 in particular, it is stated that inaddition to the method, the invention relates thus to an apparatus A, Bfor measuring the physical exercise condition, that is, exertionendurance, that is, performance of a subject to be measured. Theapparatus comprises means 1 to 4 for detecting resting heartbeat and forsending it to the calculation unit 8, 200 included also in theapparatus. The apparatus also comprises means 201 for supplyingphysiological input parameters representing the subject to be measuredto the calculation unit 8, 200 for calculating an output quantityrepresenting condition, such as maximal oxygen uptake ability, conditionclassification or any other such indicator of physical exercisecondition, on the basis of the physiological features and the heartbeatdata also supplied to the calculation unit 8, 200. The apparatus alsocomprises means, such as a display 10 and/or a memory 9, for indicatingand/or storing physical exercise condition, that is, exertion enduranceobtained as a result of calculation.

In a preferred embodiment of the invention, the apparatus A, B comprisesa transmitter unit A and a receiver unit B connected thereto by aninductive, optical or some other wireless telemetric coupling. Thetransmitter unit A comprises means 1 to 4 for detecting and sendingheartbeat signals, and the receiver unit B comprises means 5 forreceiving heartbeat signals from the transmitter unit A, and saidcalculation unit 8, 200, and said means 201 for supplying thephysiological input parameters to the calculation unit 8, 200, and saidmeans 10 for displaying and/or storing the result of calculation.

As regards an apparatus, the apparatus A, B implementing the method canbe realized in may ways but the Applicant has observed that the mostpractical and advantageous method is as in FIG. 8 which shows a wriststrap of the heartrate monitor receiver unit B. In this case theheartrate monitor receiver unit B includes at least the calculation unit8, 200, the supply means 201 of physiological data and the displayand/or the memory. The transmitter unit A included in the apparatus hasa wireless coupling to the heartrate monitor receiver unit B. Theoperation of the transmitter unit A can be integrated into the wriststrap of the heartrate monitor receiver unit B if the measurement ofheartbeat on the wrist, for example, is reliable enough. It can be seenin FIG. 8 that the wrist strap of the heartrate monitor receiver unit Bis on a wrist 600 of a human being 500, and the transmitter unit A is onthe human body, particularly on a chest 700 of the human being 500 as aso-called electrode belt.

In a preferred embodiment of the invention, the apparatus, preferablythe calculation unit 8, 200 comprises means 8 by which one or more ofthe following resting heartbeat parameters, such as mean heartbeatinterval, standard deviation of heartbeat intervals, maximum heartbeatinterval, are determined as input parameters from resting heartbeat.

In a preferred embodiment of the invention, the calculation unit 200comprises a calculation formula determined by the neural networkconstruction NN.

The preferred embodiments of the invention disclosed above and the othermore detailed solutions will improve the accuracy, speed and usabilityof the method of the invention.

The apparatus can also be realized by using a portable, transferable orfixed computer equipment to which input parameters are supplied directlyor indirectly.

Although the invention has been described above with reference to theexamples illustrated in the accompanying drawings, it will be clear thatthe invention is not restricted thereto, but it can be modified in manyways within the inventive concept disclosed in the appended claims.

What is claimed is:
 1. An apparatus for measuring an exertion enduranceindicator representing an exercise condition of a subject to bemeasured, said apparatus comprising: a heartbeat monitor for detecting aresting heartbeat of said subject and generating a heartbeat signal inresponse thereto; means for supplying selected physiological parametersassociated with said subject; a calculation unit for generating anexertion endurance signal in response to said heartbeat signal and saidphysiological parameters according to a predetermined calculationformula, said calculation unit being operatively connected to saidheartbeat monitor and including at least one input for receiving saidphysiological parameters from said means for supplying physiologicalparameters, said exertion endurance signal representing the exercisecondition of said subject; a neural network trained with a sufficientnumber of empirical measurement results comprising a plurality ofphysiological parameters and resting heartbeat parameters, and aplurality of corresponding exertion endurance indicators, saidcalculation formula being determined by said neural network; and adisplay operatively connected to said calculation unit for indicatingthe exercise condition of said subject in response to said exertionendurance signal.
 2. The apparatus of claim 1, further comprising memoryfor storing said exercise condition of said subject.
 3. The apparatus ofclaim 1, wherein said calculation unit and said neural network areformed as a microprocessor for running a predetermined applicationprogram.
 4. The apparatus of claim 1, wherein said physiologicalparameters are selected from the group consisting of sex, age, heightand weight.
 5. The apparatus of claim 1, wherein said exertion endurancesignal represents the maximal oxygen uptake ability of said subject. 6.The apparatus of claim 1, wherein said heartbeat monitor comprises: asensor for detecting a heartbeat signal from said subject to bemeasured; a transmitter operatively connected to said sensor forwirelessly transmitting said heartbeat signal; and a receiver forwirelessly receiving said heartbeat signal from said transmitter.
 7. Theapparatus of claim 6, further comprising a wrist unit, wherein at leastsaid receiver, said means for supplying physiological parameters, saidcalculation unit and said display are integrated with said wrist unit.8. A method for measuring a physical exercise condition of a subject tobe measured, the method comprising the steps of: measuring a restingheartbeat of said subject and determining therefrom at least one restingheartbeat parameter; supplying at least one physiological parameterassociated with said subject to be measured; calculating at least oneexertion endurance indicator according to a predetermined calculationformula, said calculation formula using, as input parameters to saidformula, said at least one resting heartbeat parameter and said at leastone physiological parameter, said at least one exertion enduranceindicator representing the physical condition of said subject withoutusing any input parameters obtained from exercise performed by thesubject to be measured.
 9. The method of claim 8, further comprising thestep of displaying said at least one exertion endurance indicator. 10.The method of claim 8, further comprising the step of storing said atleast one exertion endurance indicator.
 11. The method of claim 8,wherein said at least one physiological parameter is selected from thegroup consisting of sex, age, height and weight.
 12. The method of claim8, wherein said at least one exertion endurance signal represents themaximal oxygen uptake ability of said subject.
 13. The method of claim8, wherein said at least one resting heartbeat parameter is selectedfrom the group consisting of mean heartbeat interval, standard deviationof heartbeat intervals, minimum mean heartbeat interval and maximum meanheartbeat interval.
 14. The method of claim 8, further comprising thestep of providing a calculation unit, said calculation unit generatingsaid at least one exertion endurance indicator in response to said atleast one resting heartbeat parameter and said at least onephysiological parameter.
 15. The method of claim 14, further comprisingthe step of providing a heart rate monitor, wherein said calculationunit is integrated in a portion of said heart rate monitor.
 16. Themethod of claim 8, further comprising the step of providing amicroprocessor, wherein said step of calculating at least one exertionendurance indicator is performed by a software application programrunning on said microprocessor.
 17. A method for measuring a physicalexercise condition of a subject to be measured, the method comprisingthe steps of: measuring a resting heartbeat of said subject anddetermining therefrom at least one resting heartbeat parameter;supplying at least one physiological parameter associated with saidsubject to be measured; providing a neural network; causing said neuralnetwork to generate a calculation formula; calculating at least oneexertion endurance indicator according to said calculation formulagenerated by said neural network, said calculation formula using, asinput parameters to said formula, said at least one resting heartbeatparameter and said at least one physiological parameter, said at leastone exertion endurance indicator representing the physical condition ofsaid subject.
 18. The method of claim 17, further comprising the step ofdisplaying said at least one exertion endurance indicator.
 19. Themethod of claim 17, further comprising the step of storing said at leastone exertion endurance indicator.
 20. The method of claim 17, whereinsaid at least one physiological parameter is selected from the groupconsisting of sex, age, height and weight.
 21. The method of claim 17,wherein said at least one exertion endurance signal represents themaximal oxygen uptake ability of said subject.
 22. The method of claim17, wherein said at least one resting heartbeat parameter is selectedfrom the group consisting of mean heartbeat interval, standard deviationof heartbeat intervals, minimum mean heartbeat interval and maximum meanheartbeat interval.
 23. The method of claim 17, wherein the step ofproviding a neural network includes providing a neural network which hasbeen trained with a sufficient number of empirical measurement resultscomprising a plurality of physiological parameters and resting heartbeatparameters, and a plurality of corresponding exertion enduranceindicators.
 24. The method of claim 17, wherein the step of measuring aresting heartbeat is performed during a period in the range of betweentwo and five minutes.
 25. The method of claim 17, further comprising thestep of providing a microprocessor, wherein said steps of providing aneural network, causing said neural network to generate a calculationformula and calculating at least one exertion endurance indicator areperformed by a software application program running on saidmicroprocessor.
 26. The method of claim 17, further comprising the stepsof: preclassifying said at least one physiological parameter associatedwith said subject and creating a solution range based on said at leastone physiological parameter within which said at least one exertionendurance indicator value, determined from said calculating step, ispredicted to lie; and adjusting said at least one exertion enduranceindicator value in response to said at least one resting heartbeatparameter such that said at least one exertion endurance indicator valueis within said solution range.
 27. The method of claim 17, wherein saidstep of causing said neural network to generate a calculation formula isperformed using at least one fuzzy logic rule, wherein said at least oneexertion endurance indicator, determined from said calculating step, ismade fuzzy.
 28. An apparatus for measuring an exertion enduranceindicator representing an exercise condition of a subject to bemeasured, said apparatus comprising: a heartbeat monitor for detecting aresting heartbeat of said subject and generating a heartbeat signal inresponse thereto; means for supplying selected physiological parametersassociated with said subject; a calculation unit for generating anexertion endurance signal in response to said heartbeat signal and saidphysiological parameters according to a predetermined calculationformula, said calculation unit being operatively connected to saidheartbeat monitor and including at least one input for receiving saidphysiological parameters from said means for supplying physiologicalparameters, said exertion endurance signal representing the maximaloxygen uptake ability of said subject; and a display operativelyconnected to said calculation unit for indicating the exercise conditionof said subject in response to said exertion endurance signal.
 29. Amethod for measuring a physical exercise condition of a subject to bemeasured, the method comprising the steps of: measuring a restingheartbeat of said subject and determining therefrom at least one restingheartbeat parameter; supplying at least one physiological parameterassociated with said subject to be measured; calculating at least oneexertion endurance indicator according to a predetermined calculationformula, said calculation formula using, as input parameters to saidformula, said at least one resting heartbeat parameter and said at leastone physiological parameter, said at least one exertion enduranceindicator representing the maximal oxygen uptake ability of saidsubject.